Techniques for more efficient LLM serving
Class notes published on May 13, 2026 cover quantization, sparsity, attention, speculative decoding, and batching to improve LLM inference and serving.
Source: Notas de aula sobre deployment eficiente de LLMs (ickma2311.github.io). Text prepared with AI from this source.
What happened and what to do
The class notes, published on May 13, 2026, cover SmoothQuant, AWQ, INT4 inference kernels, pruning and sparsity, MoE, PagedAttention, FlashAttention, speculative decoding, and batching. They present techniques and system components intended to improve LLM inference and serving.
A company can evaluate these options with tests on its models, workloads, and infrastructure, comparing quality, latency, throughput, and resource use. Based on the results, it can implement a benchmarking and monitoring pipeline and tune serving for business needs, without assuming any one technique fits every case.
How the consultancy can help
Wendelmaques can assess inference infrastructure and workloads, define a test scope, and implement and operate serving improvements, with metrics and criteria aligned to the company’s goals.
Next step
Send a short description of your inference setup to receive a scoped proposal for diagnosis and implementation.
Let's assess your project
I reply with scope and a proposal.